KR FinBert & KR FinBert SC Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small scale corpus and fine tuning with labeled data is effective for overall performance improvement. we proposed KR FinBert for the financial domain by further pre training it on a financial corpus and fine tuning it for sentiment analysis. As many studies have shown, the performance improvement through adaptation and conducting the downstream task was also clear in this experiment. Data The training data for this model is expanded from those of KR BERT MEDIUM , texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and Korean Comments dataset. For the transfer learning, corporate related economic news articles from 72 media sources such as the Financial Times, The Korean Economy Daily, etc and analyst reports from 16 securities companies such as Kiwoom Securities, Samsung Securities, etc are added. Included in the dataset is 440,067 news titles with their content and 11,237 analyst reports. The total data size is about 13.22GB. For mlm training, we split the data…
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